A recurring issue in data-rich organizations is the difficulty in capturing and maintaining crucial data context. As data usage grows and models become more intricate, locating the correct dataset can be time-consuming and cause delays.
Select Star is a platform for data discovery and metadata that continuously updates a knowledge graph of an organization’s data by examining its structure and usage. It enhances data with context like popularity, lineage, and semantic models, simplifying the process for AI and teams to find, trust, and utilize the correct data. These enhanced metadata layers are also beneficial for large language models, greatly boosting the precision of generated SQL queries.
Shinji Kim, the founder and CEO of Select Star, joins Sean Falconer to discuss solutions to metadata curation challenges, handling data context at scale, utilizing LLMs for SQL generation, emerging trends in metadata management, and additional insights.
Full Disclosure: This episode is sponsored by Select Star.
Sean has been an academic, startup founder, and Googler. He has published works on various subjects from AI to quantum computing. Currently, he is an AI Entrepreneur in Residence at Confluent, focusing on AI strategy and thought leadership. Connect with Sean on LinkedIn.
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